Measuring Vote Privacy, Revisited.
Résumé
We propose a new measure for privacy of votes. Our measure relies on computational conditional entropy, an extension of the traditional notion of entropy that incorporates both information-theoretic and computational aspects. As a result, we capture in a unified manner privacy breaches due to two orthogonal sources of insecurity: combinatorial aspects that have to do with the number of participants, the distribution of their votes and published election outcome as well as insecurity of the cryptography used in an implementation. Our privacy measure overcomes limitations of two previous approaches to defining vote privacy and we illustrate its applicability through several case studies. We offer a generic way of applying our measure to a large class of cryptographic protocols that includes the protocols implemented in Helios. We also describe a practical application of our metric on Scantegrity audit data from a real election.